This volume focuses on the abuse of statistical inference in scientific and statistical literature, as well as in a variety of other sources, presenting examples of misused statistics to show that many scientists and statisticians are unaware of, or unwilling to challenge the chaotic state of statistical practices.;The book: provides examples of ubiquitous statistical tests taken from the biomedical and behavioural sciences, economics and the statistical literature; discusses conflicting views of randomization, emphasizing certain aspects of induction and epistemology; reveals fallacious practices in statistical causal inference, stressing the misuse of regression models and time-series analysis as instant formulas to draw causal relationships; treats constructive uses of statistics, such as a modern version of Fisher's puzzle, Bayesian analysis, Shewhart control chart, descriptive statistics, chi-square test, nonlinear modeling, spectral estimation and Markov processes in quality control.
Table of Contents
Part 1 Fads and fallacies in hypothesis testing: examples - the t-test; a two-stage test-of-significance; more examples - a Kolmogorov-Smirnov test; mechanical application of statistical tests; data snooping; an appreciation of non-significant results; type I and type II errors - for decision making; type I and type II errors - for general scientists. Part 2 Quasi-inferential statistics: randomness or chaos? Hume's problem; unobservables, semi-unobservables and grab sets; is statistics a science?; grab sets and quasi-inferential statistics; concluding remarks - quasi- and pseudo-inferential statistics. Part 3 Statistical causality and law-like relationships: sense and nonsense in causal inference - examples; Rubin's model and controlled experiments; Rubin's model and observational studies; causal inference in sample survey and other observational studies; causes, indicators and latent variables. Part 4 Amoeba regression and time-series models: discovering causal structure - science now can be easily cloned; regression and time-series analysis - science or lunacy? (part I); regression and time-series analysis - science or lunacy? (part II); regression and time-series analysis - science or lunacy? (part III); statistical correlation versus physical causation. Part 5 A critical eye and an appreciative mind toward subjective knowledge: the sorry state of statistical evaluation - a case study in educational research; modeling interaction effects - a case study from the social-behavioural sciences. Part 6 On objectivity, subjectivity and probability: statistical justification of scientific knowledge and scientific philosophy; classical probability, common sense and a strange view of nature; intuition and subjective knowledge in action - the Bayes theorem (and its misuse); Bayesian time-series analysis and E. T. (extra time-series) judgment; a pursuit of information beyond the data in randomized and nonrandomized studies; women and love - a case study in qualitative/quanti